Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences
Abstract
The developed framework apportions model error to inputs, computes predictive guarantees, and enables model correctability.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 16, 2020
- Source ID
- 10.1126/sciadv.abc3204
Entities
People
- Dionisios G. Vlachos
- Jinchao Feng
- Joshua L Lansford
- Markos A Katsoulakis
Organizations
- Air Force Office of Scientific Research
- Defense Advanced Research Projects Agency
- Johns Hopkins University
- National Science Foundation
- United States Department of Energy
- University of Delaware
- University of Massachusetts Amherst